Unmanned Aerial Vehicles (UAV) can be used to great effect for the purposes of surveillance or search and rescue operations. UAV enable search and rescue teams to cover large areas more efficiently and in less time. However, using UAV for this purpose involves the creation of large amounts of data (typically video) which must be analyzed before any potential findings can be uncovered and actions taken. This is a slow and expensive process which can result in significant delays to the response time after a target is seen by the UAV. To solve this problem, it is proposed a deep model using a visual saliency approach to automatically analyze and detect anomalies in UAV video. Contextual Saliency for Anomaly Detection in UAV Video (CSADUV) model is based on the state-of-the-art in visual saliency detection using deep convolutional neural networks and considers local and scene context, with novel additions in utilizing temporal information through a convolutional LSTM layer and modifications to the base model. This model achieves promising results with the addition of the temporal implementation producing significantly improved results compared to the state-of-the-art in saliency detection. However, due to limitations in the dataset used the model fails to generalize well to other data, failing to beat the state-of-the-art in anomaly detection in UAV footage. The approach taken shows promise with the modifications made yielding significant performance improvements and is worthy of future investigation. The lack of a publicly available dataset for anomaly detection in UAV video poses a significant roadblock to any deep learning approach to this task, however despite this paper shows that leveraging temporal information for this task, which the state-of-the-art does not currently do, can lead to improved performance.

无人飞行器（UAV）可以用于监视或搜救行动。无人机使搜索和救援团队能够更有效地在更短的时间内覆盖大片区域。但是，为此目的使用无人机涉及大量数据（通常是视频）的创建，必须先进行分析，然后才能发现任何潜在发现并采取行动。这是一个缓慢而昂贵的过程，在无人机看到目标后，可能会导致响应时间显着延迟。为了解决这个问题，提出了一种深度模型，该模型使用视觉显着性方法来自动分析和检测无人机视频中的异常。用于无人机视频异常检测的上下文显着性（CSADUV）模型基于使用深度卷积神经网络进行视觉显着性检测的最新技术，并考虑了局部和场景上下文，并在通过卷积LSTM利用时域信息方面进行了新颖的添加层和对基本模型的修改。与显着性检测中的最新技术相比，该模型通过添加时间实现而获得了令人鼓舞的结果，从而产生了显着改善的结果。但是，由于所用数据集的限制，该模型无法很好地推广到其他数据，无法在无人机录像中进行异常检测时超越最新技术。所采用的方法显示出做出修改的希望，可以带来显着的性能改进，值得将来进行研究。